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#pragma once
#include <test/cpp/common/support.h>
#include <gtest/gtest.h>
#include <ATen/TensorIndexing.h>
#include <c10/util/Exception.h>
#include <torch/nn/cloneable.h>
#include <torch/types.h>
#include <torch/utils.h>
#include <string>
#include <utility>
namespace torch {
namespace test {
// Lets you use a container without making a new class,
// for experimental implementations
class SimpleContainer : public nn::Cloneable<SimpleContainer> {
public:
void reset() override {}
template <typename ModuleHolder>
ModuleHolder add(
ModuleHolder module_holder,
std::string name = std::string()) {
return Module::register_module(std::move(name), module_holder);
}
};
struct SeedingFixture : public ::testing::Test {
SeedingFixture() {
torch::manual_seed(0);
}
};
struct WarningCapture : public WarningHandler {
WarningCapture() : prev_(Warning::get_warning_handler()) {
Warning::set_warning_handler(this);
}
~WarningCapture() {
Warning::set_warning_handler(prev_);
}
const std::vector<std::string>& messages() {
return messages_;
}
std::string str() {
return c10::Join("\n", messages_);
}
void process(
const SourceLocation& source_location,
const std::string& msg,
const bool /*verbatim*/) override {
messages_.push_back(msg);
}
private:
WarningHandler* prev_;
std::vector<std::string> messages_;
};
inline bool pointer_equal(at::Tensor first, at::Tensor second) {
return first.data_ptr() == second.data_ptr();
}
// This mirrors the `isinstance(x, torch.Tensor) and isinstance(y,
// torch.Tensor)` branch in `TestCase.assertEqual` in
// torch/testing/_internal/common_utils.py
inline void assert_tensor_equal(
at::Tensor a,
at::Tensor b,
bool allow_inf = false) {
ASSERT_TRUE(a.sizes() == b.sizes());
if (a.numel() > 0) {
if (a.device().type() == torch::kCPU &&
(a.scalar_type() == torch::kFloat16 ||
a.scalar_type() == torch::kBFloat16)) {
// CPU half and bfloat16 tensors don't have the methods we need below
a = a.to(torch::kFloat32);
}
if (a.device().type() == torch::kCUDA &&
a.scalar_type() == torch::kBFloat16) {
// CUDA bfloat16 tensors don't have the methods we need below
a = a.to(torch::kFloat32);
}
b = b.to(a);
if ((a.scalar_type() == torch::kBool) !=
(b.scalar_type() == torch::kBool)) {
TORCH_CHECK(false, "Was expecting both tensors to be bool type.");
} else {
if (a.scalar_type() == torch::kBool && b.scalar_type() == torch::kBool) {
// we want to respect precision but as bool doesn't support subtraction,
// boolean tensor has to be converted to int
a = a.to(torch::kInt);
b = b.to(torch::kInt);
}
auto diff = a - b;
if (a.is_floating_point()) {
// check that NaNs are in the same locations
auto nan_mask = torch::isnan(a);
ASSERT_TRUE(torch::equal(nan_mask, torch::isnan(b)));
diff.index_put_({nan_mask}, 0);
// inf check if allow_inf=true
if (allow_inf) {
auto inf_mask = torch::isinf(a);
auto inf_sign = inf_mask.sign();
ASSERT_TRUE(torch::equal(inf_sign, torch::isinf(b).sign()));
diff.index_put_({inf_mask}, 0);
}
}
// TODO: implement abs on CharTensor (int8)
if (diff.is_signed() && diff.scalar_type() != torch::kInt8) {
diff = diff.abs();
}
auto max_err = diff.max().item<double>();
ASSERT_LE(max_err, 1e-5);
}
}
}
// This mirrors the `isinstance(x, torch.Tensor) and isinstance(y,
// torch.Tensor)` branch in `TestCase.assertNotEqual` in
// torch/testing/_internal/common_utils.py
inline void assert_tensor_not_equal(at::Tensor x, at::Tensor y) {
if (x.sizes() != y.sizes()) {
return;
}
ASSERT_GT(x.numel(), 0);
y = y.type_as(x);
y = x.is_cuda() ? y.to({torch::kCUDA, x.get_device()}) : y.cpu();
auto nan_mask = x != x;
if (torch::equal(nan_mask, y != y)) {
auto diff = x - y;
if (diff.is_signed()) {
diff = diff.abs();
}
diff.index_put_({nan_mask}, 0);
// Use `item()` to work around:
// https://github.com/pytorch/pytorch/issues/22301
auto max_err = diff.max().item<double>();
ASSERT_GE(max_err, 1e-5);
}
}
inline int count_substr_occurrences(
const std::string& str,
const std::string& substr) {
int count = 0;
size_t pos = str.find(substr);
while (pos != std::string::npos) {
count++;
pos = str.find(substr, pos + substr.size());
}
return count;
}
// A RAII, thread local (!) guard that changes default dtype upon
// construction, and sets it back to the original dtype upon destruction.
//
// Usage of this guard is synchronized across threads, so that at any given
// time, only one guard can take effect.
struct AutoDefaultDtypeMode {
static std::mutex default_dtype_mutex;
AutoDefaultDtypeMode(c10::ScalarType default_dtype)
: prev_default_dtype(
torch::typeMetaToScalarType(torch::get_default_dtype())) {
default_dtype_mutex.lock();
torch::set_default_dtype(torch::scalarTypeToTypeMeta(default_dtype));
}
~AutoDefaultDtypeMode() {
default_dtype_mutex.unlock();
torch::set_default_dtype(torch::scalarTypeToTypeMeta(prev_default_dtype));
}
c10::ScalarType prev_default_dtype;
};
inline void assert_tensor_creation_meta(
torch::Tensor& x,
torch::autograd::CreationMeta creation_meta) {
auto autograd_meta = x.unsafeGetTensorImpl()->autograd_meta();
TORCH_CHECK(autograd_meta);
auto view_meta =
static_cast<torch::autograd::DifferentiableViewMeta*>(autograd_meta);
TORCH_CHECK(view_meta->has_bw_view());
ASSERT_EQ(view_meta->get_creation_meta(), creation_meta);
}
} // namespace test
} // namespace torch
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